There’s a huge opportunity for organizations to boost their revenues, competitive position, market share and customer relationships thanks to the unprecedented amount, type and immediacy of data available today. However, skimping on data cleansing or performance runs the risk of poor data, incorrect decisions and a lack of credibility. Thus, in this sense, the importance of conventional web scraping is marginalized. There are some certifications available in the market that are very useful to understand data cleansing and transformation/wrangling, such as a Hadoop certification.
Data Cleansing
Data cleansing can proceed only after the data source is analyzed and profiled. In order to identify data consistency problems that must be resolved, data cleansing focuses on comprehensive and continual data profiling. Generally speaking, all cleansing, profiling, transformation, wrangling, discovery applications should be in terms of web-captured/extracted data. Every website should be considered as a source. From that point of view, you should use language rather than the data integration/conventional ETL slant on business data management and traditional source data. Popular best practices for data cleaning can involve (but are not limited to):
Defining a plan for data quality: The quality plan can also include some discussion with market partners, originating from the business scenario, to tease out responses to questions such as: What data extraction standards are for use? To simplify the data pipeline, what resources do we have? For ensuring good data quality, who is responsible? What data elements are important to downstream goods and processes? and What is the process to determine accuracy?
Deduplication: No source data set is complete, and duplicate rows are often submitted by source systems. The trick here is to recognize all record's "natural key," meaning the field or fields that define each row uniquely. If records with the same natural key are included in an inbound data set, all but each of the rows could be deleted.
Reformatting values: If the date fields of the source data are in the format MM-DD-YYYY and the specified date areas are in the format YYYY/MM/DD, change the source data fields to fit the desired type.
Validating accuracy: One aspect of accuracy is performing steps to help the data to be entered accurately at the collection point, such as if a product's price is only available when you place an item in a shopping cart because of an offer or if a webpage has changed and the pricing is no longer present.
Handling blank values: Are "Null," "NA," "-1," or "TBD" represented as blank values? If so, settling on a single value for the sake of continuity would help reduce confusion among stakeholders. Imputing values is a more advanced approach. This involves using populated cells in a column, such as taking the average of populated cells and assigning it to the vacant cells, to make a fair estimate of the missing values.
Threshold checking: This is a more complex technique for data cleaning. It requires comparing the historical values and record counts of a current data set. For example, let's assume a monthly claims data provider averages overall permitted sums of $2M and unique claim counts of 100K in the health care world. These sums exceed the usually estimated threshold of variance if a corresponding data load arrives with a total accepted sum of $10M and 500K specific statements, and this should prompt further inspection.
Upfront data cleaning provides downstream applications and analytics with reliable and clear data, which can improve client trust in the data. By preparing derived data by exploring, analyzing and optimizing the data content, Import's WDI aids in data cleaning. Using 100+ spreadsheet functions and formulas, it also cleans, normalizes and enriches the data.
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Data Transformation/Wrangling
For a specific business event, data wrangling (sometimes referred to as "data munging" or "data preparation") is the process of transforming cleaned data into a dimensional model. Two main components of the WDI process, preparation and extraction, are involved. The former includes CSS rendering, JavaScript processing, network traffic interpreting, etc. The latter harmonizes the data and guarantees assurance of accuracy.
Here are some best practices for data wrangling:
Begin with a limited test set: Dealing with massive data sets is one of big data's difficulties, particularly early in the transformation of data, where analysts have to iterate rapidly over many different exploratory methods. Apply random sampling to the data collection to analyze the details and layout of the planning measures in order to help tame the unruly beast of 500 million rows. This approach would dramatically speed up the exploration of data and rapidly form the basis for more transformation.
Visualize source data: It will help to bring the "current state" of the data to life by using basic graphing tools and techniques. Histograms represent distributions, scatter plots help identify outliers, line graphs over time can show patterns in key fields, and pie graphs show percentages to the total. A perfect way to illustrate exploratory findings and necessary transformations to non-technical users is also to demonstrate how data appears in visual form.
Understand the types of data and columns: It can always help with this step to have a data dictionary (a file that defines the column names, business description and data form of a data set). It is crucial to make sure that the data values currently stored in a column meet the column's business definition. For example, in a format like MM/DD/YYYY, a column called "date of birth" should be formatted. The analyst can really get to know the data by incorporating this practice with data profiling, as mentioned above.
Zero in on just the data elements required: This is where it will also help to provide a well-defined business case. Since most data sets sources contain much more columns than are actually required, it is crucial to wrangle only the columns that the business case demands. This practice's proper execution would save untold amounts of money, time and reputation.
Turn it into actionable data: The abovementioned steps give insight on the required transformations, manipulations, reformatting and calculations to transform the data from the site source into the target format. A professional analyst may create repeatable workflows that convert the market rules needed into action for data wrangling.